Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds

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Abstract

Background: A common approach to the application of epidemiological models is to determine a single (point
estimate) parameterisation using the information available in the literature. However, in many cases there is
considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and
natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different
parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood
and highly variable, and for such infections there is a need to develop and apply statistical techniques which make
maximal use of available data.
Results: A technique based on Latin hypercube sampling combined with a novel reweighting method was
developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for
estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds
which combines a continuous time stochastic algorithm with model features such as within herd variability in disease
development and shedding, which have not been previously explored in paratuberculosis models. Generated sample
parameter combinations were assigned a weight, determined by quantifying the model’s resultant ability to
reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios
such as control options. To illustrate the utility of this approach these reweighted model outputs were used to
compare standard test and cull control strategies both individually and in combination with simple husbandry
practices that aim to reduce infection rates.
Conclusions: The technique developed has been shown to be applicable to a complex model incorporating realistic
control options. For models where parameters are not well known or subject to significant variability, the reweighting
scheme allowed estimated distributions of parameter values to be combined with additional sources of information,
such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and
uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for
parameter uncertainty and combining different sources of information, and is thus expected to be useful in
application to a large number of disease systems.